code: def main(args):
      # create an importance sampler (the prior is used as the proposal distribution)
      importance = Importance(
          model, guide=None, num_samples=args.num_samples
      )
      # get posterior samples of mu (which is the return value of model)
      # from the raw execution traces provided by the importance sampler.
      print("doing importance sampling...")
      emp_marginal = EmpiricalMarginal(importance.run(observed_data))
  
      # calculate statistics over posterior samples
      posterior_mean = emp_marginal.mean
      posterior_std_dev = emp_marginal.variance.sqrt()
  
      # report results
      inferred_mu = posterior_mean.item()
      inferred_mu_uncertainty = posterior_std_dev.item()
      print(
          "the coefficient of friction inferred by pyro is %.3f +- %.3f"
          % (inferred_mu, inferred_mu_uncertainty)
      )
  
      # note that, given the finite step size in the simulator, the simulated descent times will
      # not precisely match the numbers from the analytic result.
      # in particular the first two numbers reported below should match each other pretty closely
      # but will be systematically off from the third number
      print(
          "the mean observed descent time in the dataset is: %.4f seconds"
          % observed_mean
      )
      print(
          "the (forward) simulated descent time for the inferred (mean) mu is: %.4f seconds"
          % simulate(posterior_mean).item()
      )
      print(
          (
              "disregarding measurement noise, elementary calculus gives the descent time\n"
              + "for the inferred (mean) mu as: %.4f seconds"
          )
          % analytic_T(posterior_mean.item())
      )
  
      """
      ################## EXERCISE ###################
      # vectorize the computations in this example! #
      ###############################################
      """
  
file_path: pyro/examples/inclined_plane.py
imports: import argparse
  import numpy
  import torch
  import pyro
  from pyro.distributions import Normal, Uniform
  from pyro.infer import EmpiricalMarginal, Importance
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) without return types